Network attention is the problem of generating an image based saliency map which visualizes the input regions that best explains a class activation output of a network. Gradient-based methods [1]}[2]}[3]} attempt to compute the the derivative of the class signal with respect to the input image,
while other approaches [4]} modify network architectures to capture these signals or localize attribution [5]}. Excitation backprop [6]}, contrastive EBP [6]} or truncated contrastive EBP [8]} formulate the saliency map as marginal probabilities in a probabilistic absorbing Markov chain. Layerwise relevance propagation [9]}[10]}[11]} provides network attention through a set of layerwise propagation rules theoretically justified by deep Taylor decomposition.
Latent attention networks learn an auxiliary network to map input to attention, rather than exploring the network directly [12]}.
Inversion methods [13]} seek to recover natural images that have the same feature representation as a given image. However, the same insights have not yet been applied to fine grained categorization for face recognition. Finally, black box methods have explored network attention for systems that do not have an exposed convolutional network [14]}[15]}[16]}[4]}. The approaches to XFR explored in this paper are most closely related to EBP [6]}, RISE [15]}, and methods for network attention for pairwise similarity [20]}.
